Scholarlink

<p>This paper presents a novel multi-objetive methodology for optimizing financial investment decisions. To do this, we integrate machine learning (ML) techniques into a Black–Litterman (BL) model, using conditional value at risk (CVaR) as a risk measure within a socially responsible investment framework. The proposed approach employs ML models, including long short-term memory, random forest, artificial neural networks, gated recurrent unit, and autoregressive integrated moving average (ARIMA), to forecast asset returns. These predictions are aggregated through a model based on historical performance, and they are incorporated into a Black–Litterman–CVaR model (BL–ML–CVaR). Environmental, social, and governance (ESG) criteria are used to construct portfolios that align financial returns with sustainable investment practices. Results demonstrate that the BL–ML–CVaR portfolio offers higher returns and balanced risk compared to traditional models, with the ESG-integrated version achieving competitive performance while adhering to sustainability goals. This study provides valuable insights into how ML-driven models can enhance investment strategies in some emerging sectors facing environmental challenges, offering a path toward more sustainable and responsible financial practices.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial intelligence for socially responsible investment: a multi-objective decision-making framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月19日

查看全文
  • M. Antonia Truyols-Pont,
  • Mar Arenas-Parra,
  • Amelia Bilbao-Terol

摘要

This paper presents a novel multi-objetive methodology for optimizing financial investment decisions. To do this, we integrate machine learning (ML) techniques into a Black–Litterman (BL) model, using conditional value at risk (CVaR) as a risk measure within a socially responsible investment framework. The proposed approach employs ML models, including long short-term memory, random forest, artificial neural networks, gated recurrent unit, and autoregressive integrated moving average (ARIMA), to forecast asset returns. These predictions are aggregated through a model based on historical performance, and they are incorporated into a Black–Litterman–CVaR model (BL–ML–CVaR). Environmental, social, and governance (ESG) criteria are used to construct portfolios that align financial returns with sustainable investment practices. Results demonstrate that the BL–ML–CVaR portfolio offers higher returns and balanced risk compared to traditional models, with the ESG-integrated version achieving competitive performance while adhering to sustainability goals. This study provides valuable insights into how ML-driven models can enhance investment strategies in some emerging sectors facing environmental challenges, offering a path toward more sustainable and responsible financial practices.